Secure Downlink Transmission in Cell-Free Massive MIMO System Enhanced by Intelligent Reflecting Surfaces

This paper presents the application of intelligent reflecting surfaces (IRSs) in a cell-free massive MIMO network to enhance secure transmission in the system. Multiantenna access points (APs) need to transmit information to users safely and reliably via IRSs without fully knowing the channel state information (CSI) of a multiantenna eavesdropper (Eve) or the accurate beamforming information of a jammer. Specifically, through joint optimization of the AP active beamforming (ABF) and IRS passive beamforming (PBF), the information leaked to the Eve is limited by a set threshold, and restrictions on the IRS reflection phase are considered to maximize the weighted sum rate of the system's downlink transmission. To solve this multivariate-coupled nonconvex problem, we propose a joint precoding framework under imperfect CSI, using the generalized S-procedure, fractional programming (FP), and multidimensional complex quadratic transformation (MCQT) to transform the original complex nonconvex problem into an easily solvable convex optimization problem, and an alternating algorithm to obtain the optimal solution for precoding and IRS phase shifting. Simulation results show that the proposed scheme can significantly increase the weighted sum rate of the system compared to conventional antijamming methods while revealing that the scheme is effective in enhancing secure system transmission.

Automated summary

This paper presents the application of intelligent reflecting surfaces (IRSs) in a cell-free massive MIMO network to enhance secure transmission. By jointly optimizing active beamforming of access points (APs) and passive beamforming of IRSs, the leaked information to eavesdroppers is limited and the system's downlink transmission is maximized. The proposed joint precoding framework solves the nonconvex problem through multidimensional complex quadratic transformation and convex optimization methods, and obtains the optimal solution using an alternating algorithm.